A Nonlinear Discriminative Approach to AAM Fitting

Authors: Jason Saragih and Roland Göcke

To be resented at the Eleventh IEEE International Conference on
Computer Vision ICCV2007, Rio de Janeiro, Brazil, 14-20 October 2007

Abstract

The Active AppearanceModel (AAM) is a powerful generative
method for modeling and registering deformable visual
objects. Most methods for AAM fitting utilize a linear
parameter update model in an iterative framework. Despite
its popularity, the scope of this approach is severely
restricted, both in fitting accuracy and capture range, due
to the simplicity of the linear update models used. In this
paper, we present an new AAM fitting formulation, which
utilizes a nonlinear update model. To motivate our approach,
we compare its performance against two popular
fitting methods on two publicly available face databases, in
which this formulation boasts significant performance improvements.